The application of pharmacoinformatics in enhancing pharmaceutical care of patients with cancer

THE APPLICATION OF PHARMACOINFORMATICS IN
ENHANCING PHARMACEUTICAL CARE OF
PATIENTS WITH CANCER





YAP YI-LWERN KEVIN
(B.Sc.(Pharmacy)(Hons.), NUS; M.Eng., NTU)





A THESIS SUBMITTED

FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
DEPARTMENT OF PHARMACY
FACULTY OF SCIENCE
NATIONAL UNIVERSITY OF SINGAPORE

2011
Acknowledgements
i
ACKNOWLEDGEMENTS
The present work was carried out during the period of January 2007 to the present year.
Several persons have directly or indirectly contributed in my work, and I would like to
thank them all, with special acknowledgements to the following people:

My academic supervisor, Associate Professor Chui Wai Keung (Department of

Pharmacy, National University of Singapore) gave me the idea of using
pharmacoinformatics to solve drug-related problems in my research. I particularly
enjoyed our scientific discussions which provided me with insights and inspirations for
new ideas in my research.

My academic co-supervisor, Assistant Professor Alexandre Chan (Department of
Pharmacy, National University of Singapore; Department of Pharmacy, National Cancer
Centre Singapore) gave me guidance in the field of pharmacy practice, particularly in the
areas of oncology drug interactions and chemotherapy-induced nausea and vomiting. He
also provided me with collaborations to the National Cancer Centre Singapore (NCCS) to
carry out my research. His experience as an oncology practitioner and a clinical
researcher gave me many insights as to how pharmacoinformatics could play a role in
oncology, and his views were imperative in ensuring the applicability of this research to
clinical practice.

Acknowledgements
ii
My academic co-supervisor, Professor Chen Yu Zong (Department of Pharmacy,
National University of Singapore), gave me guidance in the field of pharmacoinformatics
and machine learning strategies.

Professor Timothy Marsh and my classmates from the Department of Communications
and New Media, Faculty of Arts, National University of Singapore, for their insights and
help in establishing the "Four Pharmaco-cybernetic Maxims".

Ms. Vivianne Shih (Clinical Pharmacist, National Cancer Centre Singapore) and Ms.
Low Xiu Hui (Research Assistant, Department of Pharmacy, National University of
Singapore) helped with the recruitment of patients with cancer and the collation of
patient-related data for the research on chemotherapy-induced nausea and vomiting.

Mr. Kuo En Yi (Science Research Program, National University of Singapore) and Mr.
See Cheng Shang (Department of Pharmacy, National University of Singapore) helped
with the maintenance of the OncoRx database.

The student helpers at the 2nd Asia-Pacific Oncology Pharmacy Congress, students from
the Undergraduate Research Opportunities in Science and Scientia Programs, and final
year students from the Department of Pharmacy, National University of Singapore, who
contributed to various parts of this research project in one way or another.

Acknowledgements
iii
Last, but not least, Associate Professor Chan Sui Yung (Head, Department of Pharmacy,
National University of Singapore), my parents, friends and colleagues from the
Department of Pharmacy, Drs. Goh Cheong Hian and Yau Wai Ping, and Ms. Yap Kai
Zhen, who provided me with help and encouragement whenever needed.
Table of Contents
iv
TABLE OF CONTENTS

Acknowledgments i
Table of Contents iv
Summary x
List of Tables xii
List of Figures xv
List of Acronyms xvi

Chapter 1
Introduction 1
1.1. The roles of e-Health and the internet for health-related information 3
1.1.1. The evolving roles of informatics and the internet for cancer care 6

1.2. Pharmacoinformatics as a bridging aid to enhance the pharmaceutical care of patients with
cancer 7
1.3. Pharmacoinformatics for oncology healthcare professionals and patients with cancer 10
1.3.1. Health information technology systems 11
1.3.2. Oncology information warehouses 13
1.3.3. Cancer-related information on the World Wide Web for patients with cancer 15
1.4. Drug-related problems associated with pharmacoinformatics 18
1.4.1. Digital dehumanisation in the patient-practitioner relationship 18
1.4.2. Virtual conflicts of recommendations 20
1.4.3. Online self-prescribing 22
Table of Contents
v
1.5. The role of pharmacoinformatics in addressing drug-related problems faced by patients
with cancer 24
1.5.1. Targeting drug interactions in patients receiving chemotherapy 25
1.5.2. Targeting adverse drug reactions in patients on emetogenic chemotherapy 32
1.6. Research question and objective 34
1.7. Scope 35

Chapter 2
Establishing the need for an oncology-specific database on anticancer drug interactions 36
2.1. Determining the opinions on drug interaction sources in anticancer treatments from
pharmacy practitioners in Asia 36
2.1.1. Methodology and study design 37
2.1.2. Respondent characteristics 38
2.1.3. Practitioner sources of anticancer drug interaction information 39
2.1.4. Interaction parameters for an oncology-specific database of anticancer drug
interactions 43
2.1.5. Accuracy, clinical usefulness and usability of the oncology-specific database 46
2.1.6. Limitations of study 49

2.1.7. Study summary 52
2.2. Determining the quality of online anticancer drug interactions 53
2.2.1. Methodology and pilot testing 56
2.2.1.1. Definition of quality 56
2.2.1.2. Creation of the quality assessment tool 56
Table of Contents
vi
2.2.1.3. Database selection and pilot testing 58
2.2.1.4. Statistical analyses 60
2.2.2. Quality of drug databases from the pilot study 60
2.2.3. Limitations of pilot study 68
2.2.4. The OncoRx-IQ tool for evaluating oncology drug interactions 69
2.3. Quality considerations for developers of pharmacoinformatics tools 70

Chapter 3
Designing a pharmacoinformatics platform for oncology drug interactions on the World Wide
Web – an overview of OncoRx 72
3.1. Methodology of creating the OncoRx database 72
3.1.1. Collation and compilation of information on anticancer drugs, chemotherapy
regimens, psychotropic agents, and complementary and alternative medicines 74
3.1.2. Creation of database and graphical user interface 76
3.1.3. Mounting of database on the internet cloud 78
3.2. User search strategies for interactions in OncoRx 80
3.2.1. Search strategy using generic anticancer drug names 80
3.2.2. Search strategy using acronyms of chemotherapy regimens 83
3.3. Hierarchical development of the OncoRx search engine 84
3.4. Database statistics 86
3.4.1. Interaction statistics with psychotropic agents 89
3.4.2. Interaction statistics with complementary and alternative medicines 92
3.5. Version for handheld devices – the OncoRx-MI application 95

Table of Contents
vii
3.5.1. Features of OncoRx-MI 96

Chapter 4
Relevance and potential of OncoRx and OncoRx-MI in clinical practice 99
4.1. Certification of the database 99
4.2. Advantages of the database 102
4.3. Potential of the database in clinical practice 105
4.3.1. Drug interaction prevalence study resulting from use of the database 105
4.3.2. User statistics and feedback on the database 106
4.4. Limitations of the database 113
4.5. The OncoRx database as a pharmacoinformatics-based search engine for prevention of
drug-related problems in oncology practice 115

Chapter 5
Application of a pharmacoinformatics tool to predict potential risks for chemotherapy-induced
nausea and vomiting 117
5.1. Designing an observational study for evaluating chemotherapy-induced nausea and
vomiting in a prospective cohort of Asian patients with cancer 117
5.1.1. Study design and setting 117
5.1.2. Chemotherapy and antiemetic treatments 118
5.1.3. Definitions of chemotherapy-induced nausea and vomiting responses 120
5.1.4. Procedures and instruments for data collection 121
5.2. Pharmacoinformatic and statistical analyses of data 123
Table of Contents
viii
5.2.1. Exploratory principal component analysis and the principal variable approach for
identifying clinical predictors 124
5.3. Demographics and chemotherapy-induced nausea and vomiting characteristics of the study

population 127
5.4. Risk factors of chemotherapy-induced nausea and vomiting as clinical predictors in patients
with cancer 130
5.5. Clinically-relevant descriptors of state anxiety in patients with cancer undergoing
emetogenic chemotherapy 138
5.6. Clinical relevance of results to practitioners in oncology supportive care 145
5.7. Limitations of cohort study 150
5.8. Principal component analysis as a pharmacoinformatics tool for the prediction of
chemotherapy-induced adverse drug reactions 153

Chapter 6
Conclusions and recommendations for future work 155

Chapter 7
Publications arising from this work 161
7.1. Peer-reviewed articles 161
7.2. Published abstracts 163
7.3. Conference presentations 164



Table of Contents
ix
Chapter 8
References 168

Appendices


Summary

x
SUMMARY
Pharmaceutical care involves identifying, solving and preventing drug-related
problems (DRPs), such as drug-drug interactions (DDIs) or adverse drug reactions
(ADRs), so as to help patients make the best use of their medications. Patients
undergoing cancer chemotherapy are at risk for DDIs and ADRs such as chemotherapy-
induced nausea and vomiting (CINV). Pharmacoinformatics can be used to improve
pharmaceutical care; however, little has been done to leverage on its technologies in
oncology practice. This project aims to address the research question of whether the
pharmaceutical care of patients with cancer can be enhanced through the application of
pharmacoinformatics in oncology practice.
The main thrust of this project revolved around the creation and development of
an oncology-specific database to detect DDIs of anticancer drugs (ACDs) and
chemotherapy regimens (CRegs). The opinions of oncology practitioners regarding
whether the availability of an oncology-specific DDI database would be useful, and the
quality of online ACD interaction information, were assessed in 2 other studies. The
feedback from these 2 studies formed the basis for the creation of OncoRx – a web-based
search engine for DDIs – where drug-, regimen- and interaction-related data were
compiled from hardcopy and softcopy resources, and published literature. Its structure
was designed around 117 ACDs and 256 CRegs, and it focused on pharmacokinetic and
pharmacodynamic interactions. It can detect DDIs with 51 psychotropic agents and 166
complementary and alternative medicines, and users can select from 15 categories of
CRegs. OncoRx hosts web documents that are uploaded onto a third-party domain and
server-side scripting is used to handle DDI search enquiries. User feedback on the quality
Summary
xi
and utility of OncoRx was sought. This feedback was generally positive, with accuracy of
DDI content and clinical usefulness being highly regarded by the users.
In addition, principal component analysis (PCA) was employed to explore the
identification of the clinical predictors of CINV in a prospective, observational study

involving patients receiving emetogenic cancer chemotherapies. CINV events were
identified from patient diaries, while demographics and risk factors were collected
through patient interviews. Five risk factors (histories of alcohol drinking, chemotherapy-
induced nausea, chemotherapy-induced vomiting, fatigue, gender) and 7 anxiety
symptoms (fear of dying, fear of the worst, unable to relax, hot/cold sweats, nervousness,
faintness, numbness) were identified as potential predictors that could be useful to
practitioners who provide oncology supportive care.
This project has demonstrated how the upcoming field of pharmacoinformatics
can be used to target 2 specific DRPs in oncology. OncoRx is the first database of its kind
that is catered towards ACD and CReg interactions, and it demonstrates the usefulness of
using a pharmacoinformatics platform to target DDIs in patients with cancer. The
utilisation of PCA to identify clinical CINV predictors in patients receiving emetogenic
chemotherapies also demonstrates the usefulness of pharmacoinformatics tools in
oncology practice. The application of pharmacoinformatics can definitely enhance the
pharmaceutical care of patients with cancer, through the reduction of DRPs, such as DDIs
and ADRs.

Keywords: Chemotherapy-induced nausea and vomiting, drug interaction database,
pharmaceutical care, pharmacoinformatics, principal component analysis
List of Tables
xii
LIST OF TABLES
1.1. Classification of drug-related problems 2
2.1. Practice characteristics of the survey respondents 39
2.2. Healthcare professionals’ sources of anticancer drug interaction information 40
2.3. Sources of drug interaction information based on the practice settings of healthcare
professionals 41
2.4. Drug interaction parameters that are important in an oncology-specific drug
interaction database 44
2.5. Factors for consideration in an online anticancer drug interaction database 47

2.6. Comparison of the characteristics and evaluation criteria of 5 currently available
quality assessment tools 55
2.7. Evaluation criteria in the OncoRx-IQ tool with explanations on what each criterion
entails 57
2.8. Publisher details and uniform resource locators of the drug databases evaluated with
the OncoRx-IQ tool 59
2.9. Mean and percentage scores of the evaluated drug databases in each quality domain,
and the correlation of each domain to overall quality 62
2.10. The “Four Pharmaco-cybernetic Maxims” for designing pharmacoinformatics tools 71
3.1. Statistics of anticancer drugs and chemotherapy regimens in the OncoRx database 87
3.2. Statistics of psychotropic agents and complementary and alternative medicines in the
OncoRx database 88
3.3. Interaction statistics among the classes of psychotropics 89
List of Tables
xiii
3.4. Types and proportions of interactions between anticancer drugs and chemotherapy
regimens with psychotropic agents 91
3.5. Interaction statistics among the categories of complementary and alternative
medicines 93
3.6. Types and proportions of interactions between anticancer drugs and chemotherapy
regimens with complementary and alternative medicines 95
4.1. The 8 HONcode principles and how the Onco-informatics website complies with
these principles 101
4.2. Demographics of OncoRx users 108
4.3. Ratings of OncoRx parameters based on user feedback 111
5.1. Chemotherapy regimens received by patients in the study 119
5.2. List of risk factors analysed in the study population 122
5.3. List of anxiety characteristics in the Beck Anxiety Inventory, classified into 4 main
dimensions 123
5.4. Demographics and chemotherapy-induced nausea and vomiting characteristics of the

study population 128
5.5. Risk factors of chemotherapy-induced nausea and vomiting identified in the study
population 133
5.6. Risk factors identified as principal variables for the prediction of clinical
chemotherapy-induced nausea and vomiting endpoints 137
5.7. Anxiety symptoms identified in the study population 140
5.8. Anxiety symptoms identified as principal variables for the prediction of clinical
chemotherapy-induced nausea and vomiting endpoints 144
List of Tables
xiv
5.9. Comparison of symptom scores from the Beck Anxiety Inventory between patients
with and without the clinical endpoints 145












List of Figures
xv
LIST OF FIGURES
1.1. Diagram showing separate “trees” of patient data becoming a diverse “forest” of
health information with time, which can be combined into one “canopy” through the
internet 4

3.1. Steps of a server round trip, showing how data flows through server side technology
when a drug interaction search query is performed 72
3.2. Flowchart showing the process of creating the OncoRx database 74
3.3. Scripts for table creation and data input into a MySQL database 77
3.4. Example of the code used to create a HTML web document 78
3.5. Example of the code used to connect to the server and select the OncoRx database 79
3.6. Screenshots of the interaction search between doxorubicin and valproic acid 81
3.7. Screenshot of the results showing the characteristics of the complementary and
alternative medicine, with American ginseng as example 82
3.8. Screenshots of the results of an interaction search between the regimen AC and
fluvoxamine 84
3.9. Hierarchical system development of the OncoRx search engine 86
3.10. Example of an interaction search in OncoRx-MI 97
3.11. Web-clip icon providing a shortcut to OncoRx-MI on the iPhone 98
4.1. HONcode certification of the Onco-informatics website showing the HONcode
quality seal of approval on the website, and its listing in the MedHunt search engine 100
4.2. Interaction results between dexamethasone and phenytoin in the OncoRx database 105
5.1. Summary of how the study on chemotherapy-induced nausea and vomiting was
conducted 118


List of Acronyms
xvi
LIST OF ACRONYMS
AC A single-day chemotherapy regimen consisting of doxorubicin (60 mg/m
2
)
and cyclophosphamide (600 mg/m
2
)

AC-based Chemotherapy regimens consisting of an anthracycline and
cyclophosphamide, inclusive of the AC, FAC and FEC regimens
ACD Anticancer drug
ADHD Attention-deficit hyperactivity disorder
ADR Adverse drug reaction
AED Antiepileptic drug
AHFS American Hospital Formulary Service
ASCO American Society of Clinical Oncology
BAI Beck Anxiety Inventory
BCCA British Columbia Cancer Agency
CAM Complementary and alternative medicine
CC Complete control
CCO Clinical Care Options
CDDP40 A single-day chemotherapy regimen consisting of cisplatin at 40 mg/m
2

CDDP100 A single-day chemotherapy regimen consisting of cisplatin at 100 mg/m
2

CINV Chemotherapy-induced nausea and vomiting
CNS Central nervous system
CP Complete protection
CPEHR Certified Professional in Electronic Health Records
CPHIE Certified Professional in Health Information Exchange Systems
List of Acronyms
xvii
CPHIMS Certified Professional in Healthcare Information and Management
Systems
CPHIT Certified Professional in Health Information Technology
CR Complete response

CReg Chemotherapy regimen
CYP450 Cytochrome P450
DDI Drug-drug interaction
DRP Drug-related problem
EMR Electronic medical record
FAC A single-day chemotherapy regimen consisting of doxorubicin (50 mg/m
2
),
cyclophosphamide (500 mg/m
2
) and fluorouracil (500 mg/m
2
)
FDA Food and Drug Administration (United States)
FEC A single-day chemotherapy regimen consisting of epirubicin (75-100
mg/m
2
), cyclophosphamide (500 mg/m
2
) and fluorouracil (500 mg/m
2
)
HDI Herb-drug interaction
HEC Highly emetogenic chemotherapy
HON Health On the Net
HTML Hypertext markup language
IT Information technology
IV Intravenous
MEC Moderately emetogenic chemotherapy
NABP National Association of Boards of Pharmacy

NCCS National Cancer Centre, Singapore
List of Acronyms
xviii
NCI National Cancer Institute
NV Nausea and vomiting
OncoRx The drug interaction database created in this project for Oncology Drug
Prescriptions (Rx)
OncoRx-IQ Quality assessment tool created in this project to evaluate Oncology Drug
Prescriptions (Rx) for their Information Quality
OncoRx-MI An iPhone version of the OncoRx database that is catered towards the
Mobile Internet
PC Principal component
PCA Principal component analysis
PCNE Pharmaceutical Care Network Europe
PDA Personal digital assistant
PHP Hypertext preprocessor
PF A multi-day chemotherapy regimen consisting of cisplatin (20 mg/m
2
/day)
and fluorouracil (1000 mg/m
2
/day)
PSY Psychotropic agent
PV Principal variable
QOL Quality of life
SQL Structured query language
STAI State-Trait Anxiety Inventory
SVM Support vector machine
TCM Traditional Chinese medicine
UK United Kingdom

List of Acronyms
xix
URL Uniform resource locator
US United States
VIPPS Verified Internet Pharmacy Practice Sites
TM

Wi-Fi Wireless Fidelity
WWW World Wide Web
XELOX A single-day chemotherapy regimen consisting of oxaliplatin (130 mg/m
2
)
and capecitabine (2000 mg/m
2
)
3G 3
rd
generation









Chapter 1
1
Chapter 1

Introduction
The practice of pharmaceutical care forms the cornerstone of any health
science discipline which concerns itself with the rational use of drugs. Its concept
combines a careful blend of caring orientation with specialised therapeutic
knowledge, experiences and judgements, so as to ensure optimal medication-related
outcomes.
1,2
These outcomes include prevention or cure of diseases, elimination or
reduction of symptoms, and slowing or arresting disease processes.
2
Healthcare
professionals, particularly clinical pharmacists, apply their knowledge and
understanding of evidence-based therapeutic guidelines, evolving sciences and
relevant ethical, social and economic principles so as to provide optimal medication
therapy management in direct patient care settings. Clinical researchers, on the other
hand, aim to contribute to new knowledge which improves the patient‟s health and
quality of life (QOL).
1

Pharmaceutical care is essential in helping patients make the best use of their
medications, and is applicable and achievable in any practice setting.
2
It involves
identifying, solving and preventing potential or actual drug-related problems (DRPs)
with regards to a patient‟s drug therapy.
2,3
There are a number of definitions to DRPs,
but in essence, they can be easily understood as events or circumstances involving
drug therapies that can actually or potentially interfere with the desired health
outcome for patients.

2,4
The Pharmaceutical Care Network Europe (PCNE)
Foundation classifies DRPs in terms of problems and causes (Table 1.1),
5
and these
include adverse reactions (classification P1), dosing problems (P3), or potential drug-
drug, drug-food or drug-herb interactions (P5). A lack of knowledge or
misinterpretation of information about the drug and its use (C2 and C3) can contribute

Chapter 1
2
to such DRPs, leading to patient non-compliance, and the patients‟ safety and quality
of life can be significantly compromised if they are not treated effectively and
appropriately.

Table 1.1. Classification of drug-related problems (DRPs). Adapted with
modification from the Pharmaceutical Care Network Europe Foundation.
5


Classification
code
DRP classification
domain
Meaning of classification
Classification by Problems
P1
Adverse reaction(s)
Patient suffers from an adverse drug event.
P2

Drug choice problem
Patient gets or is going to get a wrong or no
drug for his/her disease and/or condition.
P3
Dosing problem
Patient gets more or less than the amount of
drug he/she requires.
P4
Drug use problem
Wrong or no drug taken/administered.
P5
Interactions
There is a manifest or potential drug-drug or
drug-food interaction.
P6
Other
Other types of DRPs.
Classification by Causes
C1
Drug/dose selection
Cause of DRP related to selection of drug
and/or dosage schedule.
C2
Drug use process
Cause of DRP related to the way patient
uses the drug, in spite of proper dosage
instructions (on the label).
C3
Information
Cause of DRP related to an absence or

misinterpretation of information.
C4
Patient (psychological)
Cause of DRP related to personality or
behaviour of patient.
C5
Logistics (pharmacy)
Cause of DRP related to the logistics of the
prescribing or dispensing mechanism.
C6
Other
Other causes of DRPs.

Currently, in the provision of pharmaceutical care, healthcare professionals
work in tandem to make judgements on medication use and evaluate the patients‟
potential or actual DRPs based on their perspective and knowledge of medication

Chapter 1
3
therapy. This requires access to clinical information of individual patients. Certain
therapeutic decisions, such as dosing adjustments, are made based on experience and
through trials and errors. This is not only subjective but a very time consuming
process as well. As an accountable member of the healthcare team, the clinician must
ensure that his professional actions are in the best interests of the patient, and he is
responsible for the patient‟s outcomes that result from his actions and decisions. It is
therefore an essential requirement for the clinician to make accurate, timely, safe and
effective decisions with regards to drug use in a patient.

1.1. The roles of e-Health and the internet for health-related information
Generally, the application of information technology (IT) in healthcare has

been slow. When a patient visits the doctor, his medical and health information are
stored in many different forms such as electrocardiographs, radiographs, discharge
summaries, operative reports, laboratory results and prescription records. This
information can be likened to multiple separate “trees” of patient data which can be
potentially useful for patient profiling.
6
Over the years, as the patient visits multiple
doctors and institutions, these “trees” become a diverse “forest” (Figure 1.1). With a
global trend towards an increasing elderly population, this forest is expected to grow
denser with time. There is a need to combine this diverse array of data so as to
improve on the quality of healthcare services for patients.


Chapter 1
4


Figure 1.1. Diagram showing separate “trees” of patient data becoming a diverse
“forest” of health information with time, which can be combined into one “canopy”
through the internet.

The niche role of IT has increasingly gained acceptance among practising
healthcare professionals since the beginning of the 21
st
century, defining a concept
known as “e-Health”. This term encompasses a broad meaning, and can refer to the
combined use of any electronic information and communication technology in the
healthcare sector for clinical, educational, research or administrative purposes.
7
In

essence, e-Health can be used in any situation which involves the electronic exchange
of health-related data for the purpose of increasing the effectiveness of health care
delivery. The main aims of e-Health are to provide an improvement in the quality of
patient care, increase the healthcare practitioner‟s commitment to evidence-based
medicine, as well as empower patients and consumers with the knowledge of their
medical conditions, so as to improve their communication with their healthcare
providers.
7

The internet is rapidly gaining importance not just for healthcare
professionals, but for patients as well. The emergence of the World Wide Web

Chapter 1
5
(WWW) is one of the most significant developments in the history of the internet,
8

and has affected the way in which health-related information is distributed and
accessed over cyberspace. Nowadays, healthcare professionals can access information
on the internet to help them make decisions regarding patient care. On the other hand,
many people also frequent the internet to search for drug-related and other health-
related information. As such, patients are becoming more well-informed about health-
related issues through the information which they can get over the internet. In 2006,
the Pew Internet and American Life Project
9
reported that about 80% of internet users
in the United Sates (US) (more than 113 million people) have searched for health-
related information online. Factors contributing to this growth include an increased
health literacy of the population and perceived importance of the internet as a health
information resource, especially for chronic diseases such as cancer.

10
Convergence of
information platforms has also led to the use of internet-enabled mobile phones,
creating a greater opportunity for web access. In January 2008, approximately 12.6%
of China‟s mobile phone users (50.4 million people) were reported to have used their
phones to access the WWW.
11
Newer handheld technologies, an increasing support
for hypertext and multimedia applications, and various inexpensive browsers, such as
Internet Explorer, Mozilla Firefox, Safari, Opera, Netscape, and Google Chrome,
have enabled health-related and drug-related information to reach the public in a more
convenient and hassle-free manner.
Although the advancement of technology has provided a great improvement in
the safety, quality and efficacy aspects of healthcare, implementation of these systems
have been slow in clinical practice. Healthcare professionals need tools that can
combine various sources of clinical information into one “canopy” so that they can
access the information as and when they need. The internet or the WWW provides